4 papers
Mathematical framework for perception-driven parameter choice in image denoising
Saara Isoranta, Emilia L. K. Blåsten, Lílian Ferreira de Freitas +3
We approach image denoising from a perception-driven perspective: how can we select the parameters that are best suited for human visual perception? We combine research methods in…
Automatic regularization parameter choice for tomography using a double model approach
Chuyang Wu, Samuli Siltanen
Image reconstruction in X-ray tomography is an ill-posed inverse problem, particularly with limited available data. Regularization is thus essential, but its effectiveness hinges o…
Complex Wavelet-Based Sinogram Segmentation for Metal Artifact Reduction in Cone-Beam CT
Siiri Rautio, Alexander Meaney, Salla-Maaria Latva-Äijö +4
Metal objects pose a significant challenge in cone-beam computed tomography, as their strong and energy-dependent X-ray attenuation leads to inconsistent projections and severe str…
Image Reconstruction in Cone Beam Computed Tomography Using Controlled Gradient Sparsity
Alexander Meaney, Mikael A. K. Brix, Miika T. Nieminen +1
Total variation (TV) regularization is a popular reconstruction method for ill-posed imaging problems, and particularly useful for applications with piecewise constant targets. How…